ArticleJournal of the American Medical Informatics Association : JAMIA2026
Characterizing emergency clinician engagement with social drivers of health data among patients with opioid use disorder.
Article in Journal of the American Medical Informatics Association : JAMIA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Computable Structured Phenotype Versus Large Language Model Identification of Opioid Use Disorder Using Electronic Health Record Data.Annals of emergency medicine · 2026Article
- Current state of electronic problems lists in primary care: a rapid scoping review.Family practice · 2026Article
- Comparing computable structured phenotype- versus large language model-identification of opioid use disorder using electronic health record data.medRxiv : the preprint server for health sciences · 2026Article
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5 authors.
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Abstract
objectivesTo characterize emergency department (ED) clinician engagement with electronic health record (EHR)-based social drivers of health (SDOH) data; test whether engagement differs in encounters with opioid use disorder (OUD); and, among OUD encounters, assess whether engagement is associated with medications for OUD (MOUD). MATERIALS AND
methodsWe conducted a cross-sectional study of adult ED encounters (January 2023-October 2024). OUD encounters, identified with a structured phenotype, were matched (1:2) to non-OUD encounters. Audit logs captured clinician engagement with structured SDOH questions ("SDOH Wheel"), ICD-10 Z codes in the Problem List, Social History free text, and social work notes. Engagement was any SDOH documentation or review of preexisting SDOH data during the encounter. Logistic regression estimated associations.
resultsAmong 17 103 encounters (5701 OUD; 11 402 non-OUD), clinician SDOH documentation was rare (<1%). Clinicians most often reviewed the SDOH Wheel (1103/3953; 27.9%), followed by social work notes (1711/10 670; 16.0%), Z codes (29/620; 4.7%), and Social History free text (232/6942; 3.3%). Engagement occurred in 17.1% of encounters and was higher with OUD (23.1% vs 14.1%; OR 1.84, 95% CI 1.70-2.00). Among OUD encounters, engagement was not associated with MOUD (OR 1.27, 95% CI 0.95-1.70); however, MOUD treatment varied by race and ethnicity, reflecting persistent disparities. DISCUSSION: ED clinicians infrequently documented and favored review of structured, accessible SDOH data. Engagement increased in OUD encounters yet neither predicted MOUD nor mitigated racial and ethnic treatment disparities. EHR interfaces that surface SDOH data, coupled with targeted decision supports, might influence equitable, time-sensitive ED care.
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